A new module, ORACLE 2-D,
simulating organic aerosol formation and evolution in the atmosphere has been
developed and evaluated. The module calculates the concentrations of
surrogate organic species in two-dimensional space defined by volatility and
oxygen-to-carbon ratio. It is implemented into the EMAC global
chemistry–climate model, and a comprehensive evaluation of its performance
is conducted using an aerosol mass spectrometer (AMS) factor analysis dataset
derived from almost all major field campaigns that took place globally during
the period 2001–2010. ORACLE 2-D uses a simple photochemical aging scheme
that efficiently simulates the net effects of fragmentation and
functionalization of the organic compounds. The module predicts not only the
mass concentration of organic aerosol (OA) components, but also their
oxidation state (in terms of O : C), which allows for their classification
into primary OA (POA, chemically unprocessed), fresh secondary OA (SOA, low
oxygen content), and aged SOA (highly oxygenated). The explicit simulation of
chemical OA conversion from freshly emitted compounds to a highly oxygenated
state during photochemical aging enables the tracking of hygroscopicity
changes in OA that result from these reactions. ORACLE 2-D can thus compute
the ability of OA particles to act as cloud condensation nuclei and serves as
a tool to quantify the climatic impact of OA.

Atmospheric aerosols adversely affect human health and play a significant
role in climate change on regional and global scales. Depending on their
composition, aerosols affect the energy budget of the Earth's atmosphere by
scattering and absorbing solar radiation (direct effect) and by influencing
the reflective properties of clouds, their lifetime, and precipitation
formation (indirect effects). In addition, climate change can play a vital and
complex role in the formation and removal of atmospheric particles (Trail
et al., 2013, 2014). Organic aerosol (OA) is an important
constituent of atmospheric particles contributing 20 %–90 % to the total
submicron particulate mass, depending on the region (Zhang et al., 2007).

Primary OA (POA) has been traditionally treated as nonvolatile and inert in
global-scale chemistry–climate models (CCMs). Robinson et al. (2007)
demonstrated that OA emissions are semi-volatile and most
of the emitted OA moves to the gas phase after emission due to dilution and
evaporation. On the other hand, all organic vapors are subject to
photochemical reactions with OH in the gas phase, forming organic products
with lower volatility that can recondense to the particulate phase as
secondary organic aerosol (SOA). To describe the OA gas–aerosol
partitioning, Donahue et al. (2006) developed the
volatility basis set (VBS) framework, in which OA is assumed to be
semi-volatile and photochemically reactive and is distributed in
logarithmically spaced volatility bins. With this innovative approach,
semi-volatile primary emissions, chemical aging, and SOA formation
were unified within a common framework that is ideally suited for regional
and global chemical modeling. Since 2006, many regional (Lane et al.,
2008; Murphy and Pandis, 2009; Tsimpidi et al., 2010,
2011; Ahmadov et al., 2012; Athanasopoulou et al., 2013; Koo et al.,
2014; Fountoukis et al., 2014; Ciarelli et al., 2017; Gao et al., 2017) and
global (Pye and Seinfeld, 2010; Jathar et al., 2011; Jo et al.,
2013; Tsimpidi et al., 2014; Hodzic et al., 2016) modeling studies have used
the VBS to account for the semi-volatile nature and chemical aging of
organic compounds, demonstrating improvements in reproducing the OA budget
and its chemical resolution.

The chemical aging of OA results in significant changes to its physical and
chemical properties due to the addition of oxygen atoms from reaction
with OH. This increase in OA oxygen content is important for its impact on
climate through changes in cloud condensation nuclei (CCN) and ice nuclei (IN)
activity. In fact, oxygen content, expressed by the ratio of oxygen
to carbon atoms (O : C), influences OA hygroscopic growth (Chang et
al., 2010; Lambe et al., 2011) which affects CCN activity. In addition, the
phase-state changes of SOA during its atmospheric lifetime, which can impact
the IN activity, is also influenced by the O : C of OA (Shiraiwa et al., 2017).
Donahue et al. (2011) extended the original one-dimensional VBS framework
to two dimensions (2-D VBS), tracking not only the saturation concentration
but also the oxygen content of OA during atmospheric transport. This
approach further improved the description of the atmospheric evolution of OA
and its precursor gases that become increasingly more oxidized, less
volatile, and more hygroscopic during their atmospheric aging. However, the
large number of additional surrogate organic compounds required by the 2-D
VBS framework has hindered implementation in three-dimensional atmospheric
models (Napier et al., 2014). Therefore, the 2-D VBS approach
has been mostly adopted in box and 1-D Lagrangian models (Murphy et al.,
2011, 2012; Chacon-Madrid et al., 2013; Zhao et al., 2015; Paciga
et al., 2016). Koo et al. (2014) introduced a hybrid VBS
approach for use in three-dimensional chemical transport models (CTMs) that
combines the simplicity of the VBS with the ability to track the evolution
of OA in the 2-D space of volatility and oxygen content.

In this work, a computationally efficient module for the description of OA
composition and evolution in the atmosphere (ORACLE;
Tsimpidi et al., 2014) has been extended to allow for the first time in a
global CCM the description of both the volatility and oxygen content of
OA based on the 2-D VBS approach. Similar to ORACLE v1.0, the interface of
the new version allows the user to have full control of the complexity of
the OA scheme by adjusting the number of species and reactions (i.e., number
of compounds, volatility bins, O : C bins) to optimize the computational cost
according to the application and the desired chemical resolution. The
updated ORACLE module can provide valuable information about the
physicochemical evolution of OA during its atmospheric lifetime in support
of modeling studies and help quantify the climatic impact of OA.

2.2 ORACLE module

2.2.1 Module description

ORACLE is a computationally efficient module for the description of organic
aerosol composition and evolution in the atmosphere (Tsimpidi et al., 2014)
that has been incorporated into the EMAC model. The original version of
ORACLE (V1.0) simulated the volatility distribution of a wide variety of
semi-volatile organic surrogate compounds using bins of logarithmically
spaced effective saturation concentrations. Organic emissions from multiple
anthropogenic and natural sources are taken into account using distinct
surrogate species for each source category. These surrogates can be
subdivided into groups of organic compounds based on their volatility:
low-volatility organic compounds (LVOCs,
C*≤10-1µg m−3), semi-volatile organic compounds
(SVOCs, 10-1<C*≤102µg m−3),
intermediate-volatility organic compounds (IVOCs,
102<C*≤106µg m−3), and volatile organic
compounds (VOCs, C*>106µg m−3). These organic
compounds are allowed to partition between the gas and aerosol phases,
resulting in the formation of OA. The change in aerosol mass of each size
mode after the phase partitioning is determined by using a weighting factor
as described in Tsimpidi et al. (2014). Gas-phase photochemical reactions
that modify the volatility of the organics are taken into account and the
oxidation products of each group of precursors are simulated separately in
the module to keep track of their origin. The model results for the different
organic components in the particulate phase were compared with factor
analysis results derived from a comprehensive dataset of aerosol mass
spectrometer (AMS) measurements from multiple field campaigns across the
Northern Hemisphere. The resulting good agreement between campaign average
concentrations and model predictions supports the capability of the model to
capture the spatial and temporal characteristics of OA levels. Tsimpidi et
al. (2017) conducted an extensive sensitivity analysis of the EMAC OA
predictions to uncertain parameters in the ORACLE module. One of the major
conclusions of their analysis was that the model performance can be improved
by assuming that freshly emitted organic compounds are relatively hydrophobic
and become increasingly hygroscopic due to oxidation. As a first step to
achieve this goal, the ORACLE module has been further developed here to
account for the oxidation state of the organic surrogate compounds. The new
version is called ORACLE 2-D.

2.2.2 Emission inventory of OA precursors

Emissions of biogenic VOCs (i.e., isoprene and monoterpenes) are
calculated online by EMAC with the ONLEM submodel. The open biomass burning
emissions of organic compounds from savanna and forest fires are based on
the Global Fire Emissions Database (GFED
version 3.1; van der Werf et al., 2010) and are distributed into LVOCs
(20 %), SVOCs (40 %), and IVOCs (40 %) based on the emission factors
proposed by May et al. (2013). Emissions of
anthropogenic LVOCs, SVOCs, IVOCs, and VOCs (i.e., aromatics, alkanes,
olefins) from fossil and biofuel combustion are derived from the CMIP5
emission inventory for the RCP4.5 scenario (Thomson et al., 2011). The volatility
distribution of anthropogenic emissions to LVOCs (8 %), SVOCs (72 %),
and IVOCs (170 %) is based on the findings of Robinson et
al. (2007) and includes increased factors (the sum of the emission factors
is 250 %) to account for missing IVOC emissions from the traditional
inventories. More details about the organic compound emissions used here can
be found in Tsimpidi et al. (2016).

3.1 Module overview

The original ORACLE v1.0 (called hereafter ORACLE) uses saturation
concentration bins to describe the volatility distribution of the major OA
components. In this work, ORACLE is extended to also resolve the oxygen
content of OA expressed by the O : C ratio (ORACLE v2.0; called hereafter
ORACLE 2-D). The volatility dimension is discretized in up to
10 logarithmically spaced volatility bins separating the organic compounds
into low-volatility (LVOCs, expressed by the volatility bins of 10−3,
10−2, and 10−1µg m−3), semi-volatile (SVOCs,
C* equal to 100, 101, and 102µg m−3),
and those of intermediate volatility (IVOCs, C* equal to 103,
104, 105, and 106µg m−3). Extremely
low-volatility organic compounds (ELVOCs, C*≤10-4) can be formed by
the ozonolysis of monoterpenes and sesquiterpenes (Liggio et al., 2010; Sun
et al., 2009) playing an important role for the formation and growth of new
particles created in situ in the atmosphere by nucleation (Ehn et al., 2014).
The production of ELVOCs from biogenic VOCs is not currently included in
ORACLE since the simulation of new particle formation is outside the scope of
the current article and part of work in progress. In addition, the oxygen
content dimension is discretized in up to 20 linearly spaced O : C bins
subdividing the organic compounds into fresh emissions (expressed by the
O : C bins of 0.1 and 0.2), less oxygenated organic compounds (O : C
equal to 0.3, 0.4, 0.5, and 0.6), moderately oxygenated organic compounds
(O : C equal to 0.7, 0.8, 0.9, and 1.0), and highly oxygenated species with
O : C > 1. The first bin also includes hydrocarbons with zero oxygen.
The O : C range can be extended up to 2 (for CO2). However, there
are only a few atmospheric organic compounds with O : C higher than unity
and the observed O : C of ambient OA rarely exceeds 1.1 (Ng et al., 2010;
Kroll et al., 2011). The ability of ORACLE 2-D to simulate the degree
of oxidation of OA allows for the simulation of its hygroscopicity by using
proposed parameterizations that link the hygroscopicity parameter kappa with
the O : C of OA (Chang et al., 2010; Lambe et al., 2011; Kuwata and Lee,
2017). In the current application, the hygroscopicity of each OA compound is
represented by a linear function of the form κorg=0.18
(O : C) + 0.03 (Lambe et al., 2011).

Figure 1The 2-D grid space in the ORACLE 2-D module with saturation concentration
(in µg m−3) on the x axis and the O : C ratio on the y axis.
For each cell an organic surrogate compound is defined with a specific carbon
number calculated as a function of effective saturation concentration and
O : C ratio. The formation and evolution of SOA from (a) fuel
combustion SVOCs and IVOCs, (b) biomass burning SVOCs and IVOCs,
(c) anthropogenic VOCs and, (d) biogenic VOCs are shown. The
arrows correspond to aging reactions and the grey grids to the initial
chemical state of the species prior to aging.

ORACLE 2-D has a flexible interface in which the user can choose the
resolution (number of bins used in each dimension) of the 2-D VBS space
through a namelist file depending on the desired application and scientific
goals. The namelist file includes the variables that control the desired
chemical resolution of OA (number of volatility and O : C bins, number of
size modes, saturation concentrations, molecular weights, etc.) and the
desired emission factors for the distribution of POA emissions into LVOC,
SVOC, and IVOC volatility bins (and the corresponding O : C bins). Changes
to the gas-phase chemistry (e.g., photochemical reaction rate constants) need
to be made in the MECCA submodel by modifying the ORACLE replacement file,
which will automatically update the gas-phase chemistry used by EMAC. Then,
the interface layer of ORACLE 2-D reads the namelist variables and
automatically (i) defines the new tracers for organic compounds,
(ii) performs the coupling with the emission modules and with the gas-phase
chemistry module (MECCA), and (iii) calls the core layer of the
ORACLE 2-D module. The core layer calculates the bulk equilibrium gas
and aerosol concentrations and distributes the change in the bulk aerosol
concentration into size modes.

In this work, we employ ORACLE 2-D based on the sensitivity analysis
results of Tsimpidi et al. (2017) and using the 2-D VBS at a resolution
suitable for medium-term simulations with global chemistry–climate models.
This chemical resolution includes 150 organic aerosol surrogate compounds
compared to 34 OA compounds in ORACLE, which results in a 16 % increase in
the overall EMAC computational burden. The performance reduction comes mainly
from the gas-phase chemistry and secondarily from the atmospheric transport
of the additional tracers, since ORACLE 2-D uses an efficient method
to minimize the computational cost of the phase partitioning calculations as
described in Sect. 3.4.

3.2 Constructing the two-dimensional grid

For the current application, ORACLE 2-D distributes the OA surrogate
species into logarithmically spaced volatility bins with C* varying from
10−2 to 106µg m−3 and linearly spaced oxygen
content bins with O : C varying from 0.1 to 1.2 (Fig. 1). Each of the OA
surrogate species in this C*–O : C 2-D space is characterized by a
representative number of carbon atoms per molecule (nC) and a
molecular weight (MW). Donahue et al. (2011) used structure activity
relationships (Pankow and Asher, 2008) to express C* as a function
of nC and the number of oxygens per molecule (nO):

(1)log10C*=0.47525-nC-2.3nO+0.6nCnOnC+nO.

Given that nO is a function of O : C and nC:

(2)nO=nC(O:C).

nC can be expressed as a function of C* and O : C:

(3)nC=11.875-log10C*0.475+2.3(O:C)-0.6(O:C)1+(O:C).

Assuming that the organic compounds consist entirely of carbon, oxygen, and
hydrogen atoms (i.e., ignoring nitrogen and sulfur), the MW of each surrogate
species is a function of nC, nO, and the number of hydrogen
atoms per molecule (nH) and can therefore be calculated as

(4)MW=nH+16nO+12nC,

where

(5)nH=nC(H:C).

H : C is the atomic ratio of hydrogen to carbon approximated by Heald et
al. (2010) as

(6)H:C=2-(O:C).

Combining Eqs. (4), (2), and (5), we get

(7)MW=(15(O:C)+14)nC.

Given that nC decreases as C* increases (Eq. 3), the MW calculated
by Eq. (7) is consistent with the molecular corridor approach
(Shiraiwa et al., 2014), which suggests a tight inverse
correlation between volatility and molar mass constrained by boundary lines
of low and high O : C ratios.

Organic compound emissions from anthropogenic fuel combustion and open
biomass burning include LVOCs (with C* at 298 K equal to
10−2µg m−3),
SVOCs (with C* at 298 K equal to 100 and
102µg m−3), and IVOCs (with C* at 298 K
equal to 104 and 106µg m−3). Their corresponding
emissions are estimated using the emission factors of Tsimpidi et al. (2016).
Freshly emitted LVOCs, SVOCs, and IVOCs from
anthropogenic and open biomass burning sources are assigned an initial O : C
of 0.1 (Fig. 1a) and 0.2 (Fig. 1b), respectively (Donahue et al., 2011).
We distinguish anthropogenic and biogenic VOCs, and their first-generation
oxidation products are distributed in four volatility bins (with C* at
298 K equal to 100, 101, 102, and 103µg m−3)
by using the aerosol mass yields by Tsimpidi et al. (2014). The
O : C distributions of the first-generation VOC oxidation products are given
by Murphy et al. (2011) and vary with volatility (Fig. 1c and d).

3.3 New photochemical aging scheme

Similar to ORACLE, multiple generations of homogeneous gas-phase reactions
with OH are simulated for all OA compounds treated by ORACLE 2-D.
After each oxidation step, oxygen atoms are added to the reacting organic
gas, resulting in an increase in oxygen content and a change in volatility
due to functionalization (reducing the volatility) or fragmentation
(increasing the volatility). To minimize the computational cost and at the
same time simulate the net effect of both fragmentation and
functionalization, ORACLE 2-D uses an approach similar to the aging
scheme proposed by Murphy et al. (2011). ORACLE 2-D assumes a net
average decrease in volatility of aSOA-v (SOA from the oxidation of
anthropogenic VOCs) reacting with OH by a factor of 10 and the addition of
one or two oxygen atoms with the same probability of 50 % (Fig. 1c).
Assuming volatility transformations only into one direction
(functionalization) and neglecting fragmentation can lead to increasingly
higher OA concentrations in lower-volatility bins. This may result in an
overestimation of OA at long aging timescales up to thousands of kilometers
downwind of the source regions. However, this overestimation can be partially
offset by the conservative aging scheme used in ORACLE 2-D compared to
the relatively aggressive functionalization scheme proposed by Murphy et al.
(2012). The same addition of oxygen atoms (one or two) is assumed for
bSOA-v (SOA from the oxidation of biogenic VOCs); however, its
volatility remains unchanged due to a balancing of fragmentation and
functionalization effects (Murphy et al., 2012) (Fig. 1d). The OH reaction
rate constant for both aSOA-v and bSOA-v is
1×10-11 cm3 molecule−1 s−1 (Donahue et al., 2006;
Tsimpidi et al., 2010). The reaction of SOA-sv (SOA from the oxidation
of SVOCs) and SOA-iv (SOA from the oxidation of IVOCs) with OH with a rate
constant of 2×10-11 cm3 molecule−1 s−1 (Tsimpidi
et al., 2014) results in the addition of two or three oxygen atoms (with
equal probability) and the reduction of their volatilities by a factor of 100
(Fig. 1a and b). The number of added oxygen atoms (nO+) due to
reaction with OH is then expressed as an increase in O : C in the 2-D
space. Each of the OA surrogate compounds in the 2-D space described in
Sect. 3.3 has a representative carbon number per molecule (nC).
Assuming that carbon is conserved during the reaction with OH, the O : C of
the product is calculated as follows:

(8)(O:C)product=(O:C)reactant+nO+nC.

If the (O : C)product has more than one decimal place,
then it is distributed between the two adjacent O : C bins of the 2-D space by
using linear interpolation. Finally, since carbon is conserved, the increase
in organic mass (OM) due to the added oxygen after each oxidation reaction
is calculated as

(9)OMproductOMreactant=OMOCproductOMOCreactant,

where, following Murphy et al. (2011),

(10)OMOC=1+1612(O:C)+112(H:C).

3.4 Phase partitioning calculations

ORACLE 2-D uses the core layer of the ORACLE module to calculate the
partitioning of organic compounds between the gas and particle phases by
assuming bulk equilibrium (Tsimpidi et al., 2014). However, the computational
time required for phase partitioning increases super-linearly with the number
of species. As the condensation and/or evaporation of organic compounds
depends only on their saturation concentration and not their O : C ratio,
only one equilibrium calculation is performed per volatility bin of each
category (i.e., fuel combustion, biomass burning, anthropogenic VOC products,
and biogenic VOC products). This approach significantly reduces the number of
equations to be solved and the corresponding computational cost of the phase
partitioning calculations.

The ORACLE core layer calculates the aerosol composition at equilibrium by
solving the following set of n nonlinear equations.

(11)Ca,i=Ct,i-xiCi*fori=1,n(12)xi=Ca,i/Mi∑i=1nCa,i/Mi.

Ct,i and Ca,i are the total and aerosol-phase concentrations
of product i in µg m−3, respectively, Ci* is the
effective saturation concentration of species i, xi is the mole fraction
of product i in the absorbing organic phase, and Mi is the molecular
weight of product i, which corresponds to the weighted-average molecular
weight of the species with the same saturation concentration.

The temperature dependence of the saturation concentrations is described by
the Clausius–Clapeyron equation:

(13)ci*=ci,0*T0TexpΔHR1T0-1T,

where ci* and ci,0* are the saturation concentrations at
temperature T and T0, respectively, R is the gas constant, and
ΔH is the enthalpy of vaporization. Here, an effective ΔH
of 30 kJ mol−1 is used for all aSOA-v and bSOA-v species
based on data for α-pinene (Pathak et al., 2007). A
ΔH of 112, 100, 88, 76, and 64 kJ mol−1 is used for
the 10−2, 100, 102, 104, and
106µg m−3 volatility bins, respectively, of all organic
compounds from fuel combustion and biomass burning sources based on data for
large saturated species commonly found in primary emissions (Donahue et al.,
2006).

Water uptake by the particulate organic phase under high relative humidity
conditions can increase the aerosol mass and decrease the mole fraction of
individual organic products, which may allow for the additional condensation of SOA
(Jathar et al., 2016). However, Eq. (12) calculates the
mole fraction of organic products only in the absorbing organic phase
without taking into account the presence of water in the aerosol phase. This
simplification can result in an underestimation of SOA in areas with high
humidity and significant SOA concentrations.

Assuming that the distribution of species in the same volatility bin in
the different O : C bins does not change due to condensation or
evaporation, the gas and aerosol concentrations of each compound in the 2-D
space after the phase partitioning calculations are given by

where SOG and SOA are the gas and aerosol concentrations of each compound,
respectively, i and j are the volatility and O : C bin index in the 2-D
space, respectively, m is the total number of O : C bins, and t and
t+Δt are the times before and after the phase partitioning
calculations, respectively.
∑j=1mSOGi,j(t+Δt) and
∑j=1mSOAi,j(t+Δt) for each volatility
bin i in Eqs. (14) and (15) correspond
to (Ct,i-Ca,i) and Ca,i
at equilibrium calculated from
Eqs. (11) and (12).

Figure 2Predicted average (a) surface and (b) zonal
concentrations of total OA (in µg m−3) during the years 2001–2010.

4.1 Total OA and O : C

Simulated total OA concentrations are high over regions affected by
anthropogenic fuel combustion and open biomass burning (Fig. 2a). The
highest annual average OA concentrations at the surface are predicted over
the densely populated areas of eastern China, northern India, and Bangladesh
(10–28 µg m−3), as well as over the tropical forest in the Congo
Basin (12–22 µg m−3). Considerably high OA concentrations are also
predicted over the tropical forests of Southeast Asia and the Amazon
(6–17 µg m−3). Strong fossil fuel combustion sources over the Arabian
Peninsula result in annual average OA concentrations of 6–10 µg m−3.
The calculated zonal average concentrations of total OA at the surface
peak over the midlatitudes of the Northern Hemisphere and the tropics
(∼4µg m−3), remaining relatively high (>2µg m−3)
up to 750 hPa of altitude (Fig. 2b). Over Europe, predicted
OA annual average concentrations at the ground level are in the 3–7 µg m−3
range. Over North America, the highest OA concentration is
simulated over southern California, the Mexico City
metropolitan area, and the southeastern USA (2–8 µg m−3). OA
concentrations in the 0.5–1 µg m−3 range are predicted over parts
of the oceans due to the long-range transport of OA from adjacent
continental sources. Non-negligible concentrations of OA can be found over
Greenland and Antarctica due to the long-range transport of organic
compounds and their condensation in the particulate phase under very low
temperatures. The calculated total tropospheric OA burden is 3.3 Tg, which
is higher compared to the calculated tropospheric burden of ORACLE v1.0 (2 Tg;
Tsimpidi et al., 2016) but is within the range of OA tropospheric
burdens (0.7 to 3.8 Tg) from 31 global CTMs reported by Tsigaridis et al. (2014).

Figure 3a depicts the annual average simulated O : C ratio of total OA at the
surface. Lower values of O : C are predicted close to OA sources, i.e., over
industrialized areas in the Northern Hemisphere and over tropical
and boreal forests. The lowest average values occur over boreal forests
(as low as 0.3) due to the limited photochemical activity over these
regions in contrast to tropical forests, where O : C is around 0.5. Over
the densely populated areas of Asia, Europe, and North America, the O : C is
about 0.3–0.4, i.e., close to the anthropogenic sources. O : C levels increase
rapidly (in excess of 0.6) downwind of the sources due to photochemical
aging of the transported OA. The highest O : C values are calculated over the
Sahara Desert and the remote Atlantic and Pacific Ocean (0.8–1); however,
over these regions, OA concentrations are low (Fig. 2). O : C ratios
increase significantly with altitude according to the model (Fig. 3b)
since organic vapors transported vertically react with OH, forming products
with higher oxygen content. Over the midlatitudes of the Northern
Hemisphere, the average O : C ratio near the surface is 0.55 and increases as
the air masses are transported aloft by approximately 0.05 for every 100 hPa (Fig. 3b).

Figure 4Predicted average surface concentration (in µg m−3) of
(a) POA and (b) SOA during the years 2001–2010.

4.2 POA and SOA

Figure 4a depicts the decadal average simulated POA concentrations at the
surface. POA concentrations are high over densely populated areas in the
Northern Hemisphere due to strong fuel combustion emissions from the
industrial, energy, residential, and transport sectors. The highest
concentrations are calculated over eastern China (3–13 µg m−3),
Bangladesh (2–8 µg m−3), and eastern Europe (1–3 µg m−3).
Open biomass burning emissions from forest, woodland, peatland,
and savanna fires result in high POA concentrations over the tropics
(3–8 µg m−3 over the Congo Basin) and boreal forests
(1–6 µg m−3 over Russia). However, a large fraction of POA evaporates due to
dilution as the air masses travel downwind from the sources, resulting in
significant reduction of the concentration (Fig. 4a). Then, the material
that is transferred to the gas phase can be oxidized and recondense to the
aerosol phase, forming SOA that persists even far from the sources (Fig. 4b).
This results in a relatively homogeneous regional distribution of SOA
with a continental background of 2 µg m−3 and high
concentrations even far downwind from anthropogenic (e.g., 7–22 µg m−3
over south and eastern Asia) and open biomass burning (e.g., 9–17 µg m−3
over Central Africa) sources. Lower concentrations are
predicted over boreal forests (∼1µg m−3) due
to minor photochemical activity.

The tropospheric burden of POA and SOA calculated by ORACLE 2-D is 0.25 and
3.05 Tg, respectively. While the value of POA is very similar to the
corresponding tropospheric burden of ORACLE (0.24 Tg), the tropospheric
burden of SOA has increased significantly (1.74 Tg in ORACLE). The
tropospheric burden of POA calculated by ORACLE 2-D is much lower than
most global CTMs in the AEROCOM intercomparison study (mean value of
0.85 Tg) (Tsigaridis et al., 2014). This difference is due to the
evaporation of POA and its conversion into SOA in ORACLE 2-D, given
that this dynamic behavior of POA is not taken into account by most global
CTMs. Consequently, the ORACLE 2-D calculated SOA tropospheric burden
is higher than most CTMs from the AEROCOM intercomparison study (mean value
of 1 Tg) (Tsigaridis et al., 2014) due to its stronger chemical production.
As indicated by Tsimpidi et al. (2016), the POA burden is underestimated by
our model, especially in the urban environment in the winter. This
underestimation was partially attributed to missing residential wood burning
emissions in our inventory (van der Gon et al., 2015).

4.3 Fresh and aged SOA

The major advantage, at least in this initial phase, of extending the ORACLE
module to describe the oxygen content of OA is the model's ability to
quantify the degree of photochemical processing of OA. The model can
distinguish fresh SOA that is relatively less oxygenated from highly
aged and oxygenated SOA. As a first approximation, OA compounds with
O : C ≤ 0.6 are considered “fresh SOA” and OA compounds with
O : C > 0.6 are considered “aged SOA”.

Figure 5 depicts the average concentrations of fresh and aged SOA and their
fractional contributions to total SOA at the surface. Fresh SOA exceeds aged
SOA close to the source areas (Fig. 5c). On the other hand, the spatial
distribution of aged SOA extends further from the sources with relatively
high concentrations even over remote continental (e.g., Sahara) and oceanic
(e.g., North Pacific) regions (Fig. 5b). Aged SOA maximizes over north
India since southerly monsoon winds, prevailing during summer, favor the
transport of aged organic compounds to the north where they can accumulate
at the foothills of the Himalaya mountain range before being removed by
convection and precipitation. The fraction of aged SOA is higher over
industrialized regions (0.4–0.7), where IVOCs comprise 70 % of total fuel
combustion emissions, and lower over the tropics (0.1–0.5), where IVOCs
represent 40 % of total open biomass burning emissions (Fig. 5d).
Overall, the tropospheric burden of fresh SOA is 1.26 Tg and of aged SOA 1.79 Tg.

The simulated vertical profiles of fresh and aged SOA are quite different
(Fig. 6). Both fresh and aged SOA concentrations are high near the surface
with zonal averages of 0.9–1.7 µg m−3 over the northern
midlatitudes and the tropics. However, at higher altitudes the oxidation of
fresh SOA continues, which leads to transformation into aged SOA.
Therefore, above 960 hPa of altitude the zonal average concentrations of fresh
SOA decrease gradually (below 1 µg m−3) and those of aged SOA
increase, exceeding in some cases 1.5 µg m−3 at 850 hPa of altitude.
Further aloft in the atmosphere (above 700 hPa of altitude) the levels of both
fresh and aged SOA are reduced significantly due to dilution and removal.

Figure 5Predicted average surface concentration (in µg m−3) of
(a) fresh SOA and (b) aged SOA and surface fraction of
(c) fresh SOA and (d) aged SOA to total OA during the years 2001–2010.

4.4 2-D space distribution

In this section, we present a new feature that comes along with the upgraded
ORACLE 2-D. Since ORACLE 2-D explicitly describes the
concentration of organic surrogate species in two-dimensional space, defined
by their volatility and O : C ratio, it can also provide as output its
distribution in this 2-D space. As an example, Fig. 7 depicts the 2-D
distribution of the average total OA concentrations during the
years 2001–2010 over central Europe and the Amazon basin. The first left
column represents all the species with
C*≤10-2µg m−3, while the top line represents all
the species with O : C ≥ 1.2.

Figure 7Predicted average fraction of OA concentration in each cell of the
ORACLE 2-D grid space to total OA concentration over (a) Europe and
(b) the Amazon basin during the years 2001–2010.

Over Europe, approximately 50 % of OA has C*=1µg m−3.
About 10 % of the OA is emitted directly in this volatility bin
as POA with O :C = 0.1, while the rest, 40 %, is SOA with higher O : C from
the aging of more volatile compounds. The volatility bin with C*=10-2µg m−3
is also important, containing 18 % of the
total OA. On the other hand, volatility bins with C*>102 contain less
than 1 % of total OA since organic species in these
high-volatility bins exist mainly in the gas phase. Furthermore, about
30 % of the total OA over Europe has O : C ≤ 0.2, mainly from direct
emissions of POA from fuel combustion sources with O : C = 0.1 (20 % of
total OA). Organic species with O :C = 0.4 are also important, representing
15 % of the total OA. Overall, 40 % of OA over Europe has 0.3 ≤ O : C ≤ 0.6
and 30 % is highly oxidized with O : C ≥ 0.7.

Over the Amazon, similar to Europe, approximately 50 % of total OA has
C*=1µg m−3, while bins with C*>102 contain only
2 % of total OA. However, volatility bins with 102≤C*≤103
are more important compared to Europe containing about 30 % of the total OA
concentrations. This discrepancy can be attributed to the high bSOA-v
concentrations over the Amazon rainforest, which remain within
higher-volatility bins during their photochemical aging compared to
aSOA-v. Furthermore, similar to Europe, about 30 % of total OA over
the Amazon has O : C ≤ 0.2, mainly affected by direct aerosol
emissions from biomass burning with O : C = 0.2 (∼20 % of total
OA). OA species with 0.3 ≤ O : C ≤ 0.6 represent 55 % of the
total OA with mostly O : C = 0.4 (35 % of total OA). On the other
hand, only 15 % of the total OA is highly oxidized with
O : C ≥ 0.7. Comparing these results to Europe shows that OA over
biomass burning areas (e.g., the Amazon) are estimated to be less oxidized
than OA over anthropogenic areas (e.g., Europe), despite the higher
photochemical activity over the tropics. This perhaps unexpected result can
be explained by the higher fraction of IVOC emissions over industrialized
regions that can increase the overall oxidation state of OA through their
multi-generational aging.

5.1 Organic aerosol concentrations

The simulated POA and SOA can be compared against AMS factor analysis results
from 61 field campaigns performed during the period 2001–2010 over urban
downwind and rural environments in the Northern Hemisphere (Fig. 8).
Information for each of these campaigns is given in Tsimpidi et al. (2016).
Factor analysis techniques classify OA into hydrocarbon-like OA (HOA),
biomass burning OA (BBOA), and oxygenated OA (OOA). HOA is assumed to
correspond to POA from fossil fuel combustion, and BBOA corresponds to POA
from biomass burning (Crippa et al., 2014). Therefore, simulated POA is
compared here against the sum of AMS HOA and BBOA (Table 1). OOA corresponds
to modeled SOA (Table 2) and can be further classified into two subtypes,
semi-volatile OOA (SVOOA) and low-volatility OOA (LVOOA) (Crippa et al.,
2014). Recent studies have suggested that the main difference between these
two OOA types is often not so much their volatility, but mostly their oxygen
content (Kostenidou et al., 2015; Louvaris et al., 2017). These two OOA types
represent distinct oxidation states with O : C of 0.33–0.67 for SVOOA and
0.67–1.00 for LVOOA (Donahue et al., 2012). Therefore, SVOOA can be compared
at least as a zero-order approximation against the simulated fresh SOA
(Table 3) and LVOOA against the simulated aged SOA (Table 4).

Table 1Statistical evaluation of EMAC POA against AMS POA (sum of HOA and
BBOA) from 61 datasets worldwide during 2001–2010.

The model reproduces the observed campaign average POA concentrations within
a factor of 2 in 40 % of the cases over urban downwind and rural locations
(Table 1, Fig. 8a). The average simulated POA concentration over the urban
downwind regions is around 0.65 µg m−3 and it decreases
further after continued transport from the sources to
0.45 µg m−3 over rural areas. Compared to AMS HOA
concentrations, modeled POA is unbiased over rural environments; however, it
is underestimated downwind of urban areas
(MB =−0.17µg m−3). The model has the best performance
during summer with MB =−0.05µg m−3 and
RMSE = 0.40 µg m−3. Compared to ORACLE (Tsimpidi et al.,
2016), ORACLE 2-D produces almost identical concentrations of POA, and
hence the model performance is unchanged.

Figure 8Scatterplots comparing model predictions to AMS measurements and their
PMF analysis for (a) POA, (b) OOA, (c) SVOOA, and
(d) LVOOA concentrations (in µg m−3) in the Northern
Hemisphere during 2001–2010. Each point represents the dataset average value
over urban downwind (in red) and rural–remote (in blue) sites. Also shown are
the 1:1, 2:1, and 1:2 lines.

Calculated SOA concentrations are higher than POA both downwind of major
urban centers (2.76 µg m−3) and rural locations
(2.53 µg m−3). The model reproduces the observed campaign
average SOA concentrations within a factor of 2 in 65 % of the cases over
urban downwind and rural locations. Simulated average SOA concentrations are
slightly low-biased compared to AMS OOA measurements downwind of urban areas
(MB =−0.22µg m−3) and over rural areas
(MB =−0.18µg m−3). While the model performs well
during spring (NMB = 11 %), summer (NMB =−10 %), and autumn
(NMB = 0 %), it strongly underestimates SOA concentrations during
winter (NMB =−76 %). This underestimation of SOA by the model is
mostly due to missing LVOOA. For the 41 campaigns in which both OOA types
were identified, EMAC reproduces fresh SOA concentrations well (compared to
SVOOA) with an MB of 0.41 and 0.32 µg m−3 downwind of urban
and in rural locations, respectively (Table 4). Furthermore, except during
winter, the model is also able to capture the seasonal variations of the
fresh SOA concentration (Table 4). However, the model underpredicts aged SOA
concentrations (compared to AMS measured LVOOA) with an MB of −0.95 and
−0.70µg m−3 over urban downwind and rural locations,
respectively. This aged SOA underestimation is also evident during the four
seasons of the year, especially during winter (NMB =−93 %). This
result may indicate that the model underestimates the atmospheric aging rate
of SOA or misses processes forming highly oxidized OA, e.g., highly oxidized
ELVOCs from the ozonolysis of terpenes (Ehn et al., 2014; Jokinen et al.,
2016). Therefore, another useful feature of ORACLE 2-D is that
detailed AMS measurements can be used to gain further insights into what
causes biases and errors in its OA predictions. Compared to ORACLE (Tsimpidi
et al., 2016), ORACLE 2-D tends to produce higher fresh SOA
concentrations and slightly lower aged SOA concentrations. However, this
discrepancy may be partially due to the fact that in ORACLE fresh SOA is
assumed to correspond only to first-generation oxidation products, while in
ORACLE 2-D fresh SOA is defined based on the O : C ratio and
includes higher-generation oxidation products as well. Overall, the simulated
average total SOA concentrations in ORACLE 2-D
(2.59 µg m−3) are higher than ORACLE
(1.91 µg m−3), reducing the gap with the corresponding AMS
OOA (2.78 µg m−3).

Figure 9Scatterplot comparing model predictions to measurements for O : C
ratio of total OA (in blue) and OOA (in red) over nonurban areas in the
Northern Hemisphere during 2001–2010. Each point represents the dataset
average value over a specific measurement site. Also shown are the 1:1, 2:1,
and 1:2 lines.

5.2 O : C ratio

The simulated O : C ratio of total OA and SOA is compared against observed O : C
of OA and OOA from 30 and 57 field campaigns, respectively, performed during
the period 2001–2010 in the Northern Hemisphere (Tables 5 and 6). Given that
global models, including EMAC, underestimate concentrations of POA and SOA
over urban locations (Tsigaridis et al., 2014; Tsimpidi et al., 2016), AMS
data from these locations are not included for the statistical evaluation of
calculated O : C (Fig. 9, Tables 7 and 8). In fact, the model tends to
overestimate the O : C of total OA compared to observations from urban
locations (e.g., Mexico City, Barcelona, New York, Riverside, Paris;
Table 5). This overprediction can be attributed to the coarse spatial resolution
and the difficulty to represent freshly emitted (or formed) OA on a local
scale. On the other hand, the model performs remarkably well in reproducing
the O : C ratio of both total OA (NMB = 7 %, RMSE = 0.13) and SOA
(NMB = 5 %, RMSE = 0.12) in nonurban areas.

The model tends to overestimate the O : C ratio of total OA compared to
observations from field campaigns close to the coasts (e.g., western coast
of Chile, Mace Head). The model reproduces the low O : C ratios of
total OA during winter and the higher values during spring and autumn very well (Table 7).
Even if the model substantially underpredicts all types of OA during
wintertime, the calculated OA O : C is similar to observations because the
relative contribution of each OA compound is captured by the model. The
slightly larger underprediction of aged SOA (compared to fresh SOA and POA)
during winter results in a small underprediction of total OA O : C
(NMB =−7 %). This indicates that the missing OA during winter cannot be
attributed only to missing POA sources (e.g., residential biofuel use) but
also to missing SOA formation pathways (e.g., multiphase oxidation). On the
other hand, it overestimates the O : C of total OA during summer (MB = 0.14).
Field campaigns conducted during the summer months provide O : C of total OA
with surprisingly low O : C values for this time of the year (0.3–0.4), which
are not captured by the model. Overall, the O : C of total OA is slightly
overestimated by the model (MB = 0.04).

Simulated O : C of SOA is relatively high (0.5–0.7) at most observational
sites, which agrees well with measurements. The model also captures the very
high O : C values (larger than 0.7) observed over very remote areas (e.g.,
Okinawa, Finokalia). On the other hand, the low O : C ratios (lower than 0.5)
reported by a few field campaigns are overestimated by the model (Duke
Forest, Rhine Valley, Jiaxing). Remarkably, the model performance during
winter is unbiased for the O : C ratio of SOA (Table 8). This is in contrast
to its inability to reproduce SOA concentrations during winter (Tsimpidi et
al., 2016). During the other seasons the model slightly overestimates the
O : C ratio of SOA with an MB ranging from 0.02 (during spring) to 0.07
(during summer and autumn).

Furthermore, the O : C ratios of total OA presented here can be used to
calculate the OM ∕ OC based on Eqs. (6) and (10). The average calculated OM ∕ OC
is 1.8 compared to the observed 1.75. This is also in accordance with the
OM ∕ OC value of 1.84 reported by Canagaratna et
al. (2015) obtained from a large dataset of chamber and ambient OA
measurements. The calculated OM ∕ OC ranges from 1.6 during winter to 2 during
autumn, while the observed O : C ranges from 1.6 during summer to 1.8 during
spring and autumn.

The ORACLE module for the description of OA composition and evolution in the
atmosphere (Tsimpidi et al., 2014) has been extended to simultaneously
simulate the volatility and the oxygen content of OA that results from
atmospheric aging. Similar to ORACLE v1.0, the new version is implemented in
the EMAC CCM and considers the formation of OA from emissions and
chemical aging of LVOCs, SVOCs, and IVOCs from fossil fuel, biofuel, and
biomass burning sources, as well as from the oxidation of
anthropogenic and biogenic VOC precursors. The updated ORACLE module employs
the 2-D VBS framework that uses logarithmically spaced effective saturation
concentration bins to describe the volatility of organic compounds and
linearly spaced oxygen-per-carbon-ratio bins to describe their oxygenation state.

The simulated concentrations of OA and its components (i.e., POA and SOA)
are similar to ORACLE v1.0 with relatively high concentrations over
industrialized areas in the Northern Hemisphere and biomass burning
areas in the tropics. The tropospheric burdens of POA and SOA are calculated
to be 0.25 and 3.05 Tg, respectively, the latter being higher than with
ORACLE v1.0. The new ORACLE 2-D module additionally allows for the calculation of
the oxidation state of OA (in terms of O : C) and therefore its
classification into fresh SOA (with O : C lower than 0.6) and aged SOA (with
O : C higher than 0.6). O : C is calculated to be relatively low close to source
regions and at high latitudes. The predicted O : C ratio is as low as 0.3 over
boreal forests, 0.3–0.4 close to the anthropogenic sources in the
Northern Hemisphere, 0.5 over the tropical forests, and higher than 0.6
downwind of source areas and at altitudes aloft. Accordingly, fresh SOA
concentrations are higher close to sources, while aged SOA increases as the
air masses are transported away from the sources and to higher altitudes.
The estimated tropospheric burden of fresh SOA is 1.26 Tg and of aged SOA
1.79 Tg. The analysis of model results regarding the distribution of OA in
the 2-D space of volatility and oxygen content showed that half of OA has
C*=1µg m−3 over both anthropogenic areas (e.g.,
Europe) and tropical forests (e.g., Amazon). Furthermore, over Europe OA
compounds are more strongly oxidized than over the Amazon and consist of
40 % fresh OA and 30 % aged SOA (compared to 55 % and 15 %,
respectively, over the Amazon). The remaining 30 % in both areas consists
of POA or very low oxidized material with O : C ≤ 0.2.

The simulated OA components (POA, fresh, aged, and total SOA) have been
compared with observed subtypes of OA (sum of HOA and BBOA, SVOOA, LVOOA, and
OOA) derived from AMS factor analysis results from 61 field campaigns
performed during the period 2001–2010 over urban downwind and rural
environments in the Northern Hemisphere. The model reproduces the POA
concentrations over both types of environment with low bias. While the model
appears to capture the fresh SOA concentrations reasonably well, it
underestimates the concentrations of aged SOA, resulting in an overall
underprediction of total SOA. While this aged SOA underestimation is evident
throughout the year, it is more pronounced during winter. The
underestimation of aged SOA by the model emphasizes the need to better
describe chemical aging processes and further explore the effect of ELVOCs
on the formation of SOA. Furthermore, the model realistically reproduces the
observed O : C ratio of total OA over urban downwind and rural areas. It
adequately captures the low values during winter and the higher values during
spring and autumn; however, it overestimates the O : C of total OA during
summer. In addition, the model slightly overestimates the O : C ratio of total
OOA (NMB = 5 %), except during winter when modeled OOA O : C is unbiased.

ORACLE 2-D is a flexible module that efficiently describes organic
aerosol composition and evolution in the atmosphere by simulating changes in
OA volatility and oxygen content throughout its lifetime in the atmosphere.
The ability of ORACLE 2-D to simulate the degree of OA oxidation can
help determine changing OA hygroscopicity during atmospheric aging.
ORACLE 2-D can potentially provide valuable insights into the
composition and reactivity of OA and the physicochemical evolution during
atmospheric transport of OA, which can help reduce aerosol-related
uncertainties that persist in global atmospheric chemistry and climate
modeling.

The complete ORACLE 2-D code can be obtained by
applying for an EMAC license or upon request by emailing the first author. To
use ORACLE 2-D as part of EMAC, please first apply for an ECHAM5 and a MESSy
license. The GCM ECHAM5 has been developed at the Max Planck Institute for
Meteorology in Hamburg (see
http://www.mpimet.mpg.de/en/science/models/mpi-esm/echam/, ECHAM5,
2018). The Modular Earth Submodel System (MESSy) is developed and applied by
a consortium of institutions initiated by the Max Planck Institute for
Chemistry. The usage of MESSy and access to the source code is licensed to
all affiliates of institutions that are members of the MESSy Consortium.
Institutions can become a member of the MESSy Consortium by signing the MESSy
Memorandum of Understanding. More information can be found on the MESSy
Consortium website (https://www.messy-interface.org/, Messy, 2018). The
measurement data used for the evaluation of the model can be found in
Tables 5 and 6 of this paper and Tables S6 and S7 in the Supplement of
Tsimpidi et al. (2016) (available online at
https://doi.org/10.5194/acp-16-8939-2016-supplement).

A new module, ORACLE 2-D, that calculates the concentrations of surrogate organic species in two-dimensional space defined by volatility and oxygen-to-carbon ratio has been developed and evaluated. ORACLE 2-D uses a simple photochemical aging scheme that efficiently simulates the net effects of fragmentation and functionalization. ORACLE 2-D can be used to compute the ability of organic particles to act as cloud condensation nuclei and serves as a tool to quantify their climatic impact.

A new module, ORACLE 2-D, that calculates the concentrations of surrogate organic species in...